Physical Sciences and Mathematics Commons^™

Front Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Rancang Bangun Aplikasi Status Gizi Bayi Berbasis Android, Ima Kurniastuti, Ahmad Syafiq Kamil

Elinvo (Electronics, Informatics, and Vocational Education)

Penelitian ini bertujuan untuk menghasilkan Aplikasi Status Gizi Bayi Berbasis Android yang mempermudah petugas kesehatan dalam menentukan status gizi bayi. Proses pembuatan dilakukan menggunakan Android Studio. Fitur dalam Android Studio yang dimanfaatkan adalah fitur TextView, fitur Radiogroup dan fitur Button. Input dalam aplikasi terdiri dari lima macam yaitu usia, berat badan, panjang bayi, lingkar kepala memanfaatkan fitur TextView sedangkan input jenis kelamin menggunakan fitur Radiogroup. Aplikasi memiliki dua tombol yaitu tombol hitung dan tombol reset. Tombol hitung berfungsi untuk memulai proses pemeriksaan dan tombol reset berfungsi untuk menghapus semua input yang dilakukan oleh pengguna sebelumnya sehingga memudahkan pengguna dalam melakukan …

Sistem Informasi Geografi (Sig) Pencarian Lokasi Tambal Ban Dengan Pemanfaatan Teknologi Gps, Desy Ika Puspitasari, Zaenuddin Zaenuddin, Fitrah Yuridka

Elinvo (Electronics, Informatics, and Vocational Education)

The occurrence of a tire leak in the middle of a trip can often be caused by many things, such as being hit by a sharp object, the age of the tire is too old, leaking in the former patches, or it can be caused by others. These factors make vehicle users panic and think to immediately find the nearest tire repair location. Almost everyone has a smartphone where there is a Google Map application by utilizing GPS technology so as not to get lost when driving, with GPS technology can be estimated the distance between one location and another. …

Analisis Dan Desain Sistem Informasi Penelitian Dan Pengabdian Masyarakat (Simpenmas) Politeknik Negeri Lhokseumawe, Hari Toha Hidayat

Elinvo (Electronics, Informatics, and Vocational Education)

Information systems of research and community service need to be developed to improve the management of research outputs and community service outcomes by lecturers while making it easier to evaluate tertiary accreditation. This system was developed using the waterfall model. This article aims to describe the stages of system development consisting of: (1) investigating user needs; (2) data and needs analysis in the process of making a system; (3) design; and (4) implementation. The result is the need for a system that specifically can provide facilities for uploading research outputs and community service such as journal publications, textbooks / texts, …

Using The Analytic Hierarchy Process For Decision Making In Smart Traffic Light For High Priority Vehicle, Muhammad Izzuddin Mahali, Eko Marpanaji, Muhammad Adi Febri Setiawan

Elinvo (Electronics, Informatics, and Vocational Education)

Kemacetan sering terjadi di banyak persimpangan jalan kota-kota besar di Indonesia. Sesuatu yang penting seperti kendaraan prioritas sering pula berada pada kemaccetan tersebut. Untuk mengatasi permasalahan tersebut terdapat inovasi baru yaitu Intelligent Traffic Light yang dibekali dengan Aplikasi "Bang Jopin". Namun terdapat permasalahan baru ketika ada kendaraan prioritas melakukan request emergency secara bersamaan pada traffic light yang sama. Penentuan prioritas tidak dapat dilakukan dengan pengurutan saja karena ketika memprioritaskan kendaraan pada traffic light harus mempertimbangkan karakteristik traffic light dan kebiasaan pengendara. Oleh kerena itu, Metode Analitical Hierarchy Process (AHP) merupakan solusi yang tepat dalam menentukan kendaraan prioritas yang didahulukan ketika …

Modeling It Of Discourse Analysis And The Ussues Machine Translation, Nilufar Z. Abdurakhmonova, Allayorzhon G. Teshabayev

Scientific Bulletin. Physical and Mathematical Research

The aim of the research work is to analyze theories on the formation of linguistic database of the translation program of simple texts from English into Uzbek and to create program foundations. The object of the research work is word combinations and simple sentences of English and Uzbek languages, grammatical expressions as well. Scientific novelty of the research work is as follows: Drawn conclusions provide exactness of translation on creating linguistic database of machine translation. created linguistic database of phrasal verbs, morphological lexicon, affixes of English and Uzbek languages and their morphological and syntactic models; identified coordination of simple sentence …

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …

Blockchain Based Access Control For Enterprise Blockchain Applications, Lei Xu, Isaac Markus, Subhod I, Nikhil Nayab

Computer Science Faculty Publications and Presentations

Access control is one of the fundamental security mechanisms of IT systems. Most existing access control schemes rely on a centralized party to manage and enforce access control policies. As blockchain technologies, especially permissioned networks, find more applicability beyond cryptocurrencies in enterprise solutions, it is expected that the security requirements will increase. Therefore, it is necessary to develop an access control system that works in a decentralized environment without compromising the unique features of a blockchain. A straightforward method to support access control is to deploy a firewall in front of the enterprise blockchain application. However, this approach does not …

Gaze Collaboration Patterns Of Successful And Unsuccessful Programming Pairs Using Cross-Recurrence Quantification Analysis, Maureen Villamor, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

A dual eye tracking experiment was performed on pairs of novice programmers as they traced and debugged fragments of code. These programming pairs were categorized into successful and unsuccessful pairs based on their debugging scores. Cross-recurrence quantification analysis (CRQA), an analysis using cross-recurrence plots (CRP), was used to determine whether there are significant differences in the gaze collaboration patterns between these pair categories. Results showed that successful and unsuccessful pairs can be characterized distinctively based on their CRPs and CRQA metrics. This study also attempted to interpret the CRQA metrics in relation to how the pairs collaborated in order to …

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.

Smart Factories, Dumb Policy? Managing Cybersecurity And Data Privacy Risks In The Industrial Internet Of Things, Scott J. Shackelford

Minnesota Journal of Law, Science & Technology

No abstract provided.

Automating Software Changes Via Recommendation Systems, Xiaoyu Liu

Computer Science and Engineering Theses and Dissertations

As the complexity of software systems is growing tremendously, it came with increasingly sophisticated data provided during development. The systematic and large-scale accumulation of software engineering data opened up new opportunities that infer information appropriately can be helpful to software development in a given context. This type of intelligent software development tools came to be known as recommendation systems.

Recommendation Systems in Software Change (RSSCs) share commonalities with conventional recommendation systems: mainly in their usage model, the usual reliance on data mining, and in the predictive nature of their functionality. So a major challenge for designing RSSCs is to automatically …

Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia

University of New Orleans Theses and Dissertations

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By …

A Domain Specific Language For Digital Forensics And Incident Response Analysis, Christopher D. Stelly

University of New Orleans Theses and Dissertations

One of the longstanding conceptual problems in digital forensics is the dichotomy between the need for verifiable and reproducible forensic investigations, and the lack of practical mechanisms to accomplish them. With nearly four decades of professional digital forensic practice, investigator notes are still the primary source of reproducibility information, and much of it is tied to the functions of specific, often proprietary, tools.

The lack of a formal means of specification for digital forensic operations results in three major problems. Specifically, there is a critical lack of:

a) standardized and automated means to scientifically verify accuracy of digital forensic tools; …

The Effects Of Automated Grading On Computer Science Courses At The University Of New Orleans, Jerod F A Dunbar

University of New Orleans Theses and Dissertations

This is a study of the impacts of the incorporation, into certain points of the Computer Science degree program at the University of New Orleans, of Course Management software with an Autograding component. The software in question, developed at Carnegie Mellon University, is called “Autolab.” We begin by dissecting Autolab in order to gain an understanding of its inner workings. We can then take out understanding of its functionality and apply that to an examination of fundamental changes to courses in the time since they incorporated the software. With that, we then compare Drop, Failure, Withdrawal rate data from before …

A Hybrid And Scalable Error Correction Algorithm For Indel And Substitution Errors Of Long Reads, Arghya Kusum Das, Sayan Goswami, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Background: Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. Methods: In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the …

A Qualitative Representation Of Spatial Scenes In R2 With Regions And Lines, Joshua Lewis

Electronic Theses and Dissertations

Regions and lines are common geographic abstractions for geographic objects. Collections of regions, lines, and other representations of spatial objects form a spatial scene, along with their relations. For instance, the states of Maine and New Hampshire can be represented by a pair of regions and related based on their topological properties. These two states are adjacent (i.e., they meet along their shared boundary), whereas Maine and Florida are not adjacent (i.e., they are disjoint).

A detailed model for qualitatively describing spatial scenes should capture the essential properties of a configuration such that a description of the represented objects …

Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar

Master's Projects

A fundamental problem in malware research consists of malware detection, that is, dis- tinguishing malware samples from benign samples. This problem becomes more challeng- ing when we consider multiple malware families. A typical approach to this multi-family detection problem is to train a machine learning model for each malware family and score each sample against all models. The resulting scores are then used for classification. We refer to this approach as “cold fusion,” since we combine previously-trained models—no retraining of these base models is required when additional malware families are considered. An alternative approach is to train a single model …

Data-Driven Multiscale Modeling Reveals The Role Of Metabolic Coupling For The Spatio-Temporal Growth Dynamics Of Yeast Colonies, Jukka Intosalmi, Adrian C. Scott, Michelle Hays, Nicholas Flann, Olli Yli-Harja, Harri Lähdesmäki, Aimée M. Dudley, Alexander Skupin

Computer Science Faculty and Staff Publications

Background: Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell–cell and metabolic coupling lead to functionally optimized structures is still limited.

Results: Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this …

Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain

Master's Projects

Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme …

Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas

SMU Data Science Review

In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. …

A Data Science Approach To Defining A Data Scientist, Andy Ho, An Nguyen, Jodi L. Pafford, Robert Slater

SMU Data Science Review

In this paper, we present a common definition and list of skills for a Data Scientist using online job postings. The overlap and ambiguity of various roles such as data scientist, data engineer, data analyst, software engineer, database administrator, and statistician motivate the problem. To arrive at a single Data Scientist definition, we collect over 8,000 job postings from Indeed.com for the six job titles. Each corpus contains text on job qualifications, skills, responsibilities, educational preferences, and requirements. Our data science methodology and analysis rendered the single definition of a data scientist: A data scientist codes, collaborates, and communicates – …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

A Data Driven Approach To Forecast Demand, Hannah Kosinovsky, Sita Daggubati, Kumar Ramasundaram, Brent Allen

SMU Data Science Review

Abstract. In this paper, we present a model and methodology for accurately predicting the following quarter’s sales volume of individual products given the previous five years of sales data. Forecasting product demand for a single supplier is complicated by seasonal demand variation, business cycle impacts, and customer churn. We developed a novel prediction using machine learning methodology, based upon a Dense neural network (DNN) model that implicitly considers cyclical demand variation and explicitly considers customer churn while minimizing the least absolute error between predicted demand and actual sales. Using parts sales data for a supplier to the oil and gas …

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chu, Weihua Zhou

Faculty Publications

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

Results: Compared with the seven conventional machine learning …